Design Food Delivery System

On-demand food delivery platform with restaurant catalog, menu/pricing, cart/checkout, courier assignment, live tracking, and notifications.

Functional requirements

  • Browse restaurants by cuisine, ratings, distance, and promotions.
  • View real-time menus with pricing, availability, and preparation time.
  • Add/remove items to cart; apply promo codes; compute delivery fees and taxes.
  • Checkout with secure payments; orders must be confirmed within seconds.
  • Automatic courier assignment with batching where possible; live order tracking.
  • Substitutions and out-of-stock handling with customer approval.
  • Order status push notifications and in-app updates across devices.
  • Restaurants receive orders on a partner app or web console with acknowledgment.
  • Cancellations, refunds, and support workflows with audit trails.
  • Order history, re-ordering, and receipts via email.

Non-functional requirements

  • Availability: 99.95% uptime; a single instance or AZ failure must not impact ordering.
  • Performance: catalog/menu reads < 100 ms p99 cache-warm; checkout confirmation < 2 s p99.
  • Scalability: independently scale catalog, checkout, and delivery assignment; handle regional dinner surges.
  • Durability: orders and payments persisted with transactional guarantees; receipts retained per compliance.
  • Consistency: strong consistency for orders/payments; eventual consistency tolerated for analytics and recommendation feeds.
  • Security: PCI-DSS scope isolated to payment service; TLS 1.2+ in transit; AES-256 at rest.
  • Resilience: circuit breakers and timeouts between services; retries with backoff and idempotency keys at checkout.
  • Observability: traces, structured logs, and metrics for API, queue depth, and assignment latency.
  • Geo: latency-based routing at the edge; store-specific menus cached per region.

How the design evolves

Stage 1: Monolith MVP

Single service handles catalog, cart, checkout, and delivery updates backed by a single database.

What was missing: Edge controls, caching, async, and fault isolation.

Why that's risky: SPOF and poor surge handling.

What gets added: Nothing yet (MVP).

Trade-offs: Fast to build but not production-ready.

Stage 2: Edge, API Gateway, and Rate Limiting

Add ingress protection and throttle abusive clients before business logic.

What was missing: No edge/WAF or throttling.

Why that's risky: Susceptible to spikes and abuse.

What gets added: Edge, API gateway, rate limiter.

Trade-offs: More moving parts.

Stage 3: Horizontal Scale and Load Balancing

Distribute traffic across app replicas behind a service LB.

What was missing: No redundancy and horizontal scale.

Why that's risky: SPOF and bottleneck.

What gets added: LB and replicas.

Trade-offs: State/session externalization needed.

Stage 4: Split Services and Add Caching

Break out catalog/cart/checkout services and add cache + dedicated databases.

What was missing: Hot reads and tight coupling across concerns.

Why that's risky: Slow UX under load and deploy blast radius.

What gets added: Cache for reads and service separation.

Trade-offs: Cache invalidation and contract management.

Stage 5: Async Pipeline, Payments, and Notifications

Use queues/workers to decouple side effects; isolate PCI-scoped payment service; add notifications.

What was missing: Checkout blocked by side effects and PCI coupling.

Why that's risky: High tail latencies and wider blast radius.

What gets added: Queue, workers, and isolated payment/delivery services.

Trade-offs: Operational complexity and event contracts.

Stage 6: Real-time Tracking, Observability, and Media

Add live courier location updates, analytics sink, and blob storage for images/receipts with CDN at the edge.

What was missing: Media offload, live tracking ingestion, and analytics sink.

Why that's risky: UI latency and missing visibility into delivery pipeline.

What gets added: CDN + Blob for media, location ingestion, analytics worker.

Trade-offs: More services and operational overhead.

Frequently asked questions

How do you keep checkout idempotent under retries?

Require an idempotency key from the client for checkout requests. Persist the key with the final order result. On retry, return the stored outcome. Ensure payment capture and order write are idempotent and use outbox/event logs for downstream publication.

How do you assign couriers efficiently during peaks?

Use a delivery service that batches nearby orders and available couriers. Maintain a priority queue by ETA/cost. Use a worker consuming assignment requests from a queue, with backoff and retries for contention. Keep courier location updates in memory for fast lookups.

How do you keep menus fresh without overloading databases?

Cache per-restaurant menus with short TTLs and invalidate on updates from the partner console. Use background refresh for top restaurants during peaks. Include ETags to avoid sending full payloads.

How do you ensure PCI compliance while keeping the system agile?

Isolate payment processing in a dedicated service behind strict network and data access controls. Tokenize card data; never store PANs directly. Use a compliant PSP. Keep the rest of the platform out of PCI scope.

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Use clear names so your design intent is easy to understand.
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